3.
Our databases and dataset • Machines dedicated to MySQL: over 175 • Thatʼs roughly how many production machines we had total a year ago • Relational data on master databases: over 11 terabytes • Unique rows: over 25 billionMassively Sharded MySQL

4.
MySQL Replication 101 • Asynchronous • Single-threaded SQL execution on slave • Masters can have multiple slaves • A slave can only have one master • Can be hierarchical, but complicates failure-handling • Keep two standby slaves per pool: one to promote when a master fails, and the other to bring up additional slaves quickly • Scales reads, not writesMassively Sharded MySQL

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Why Partition? Reason 2: Data size • Working set wonʼt ﬁt in RAM • SSD performance drops as disk ﬁlls up • Risk of completely full disk • Operational difﬁculties: slow backups, longer to spin up new slaves • Fault isolation: all of your data in one place = single point of failure affecting all usersMassively Sharded MySQL

8.
Horizontal Partitioning Divide a table by relocating sets of rows • Some support internally by MySQL, allowing you to divide a table into several ﬁles transparently, but with limitations • Sharding is the implementation of horizontal partitioning outside of MySQL (at the application level or service level). Each partition is a separate table. They may be located in different database schemas and/or different instances of MySQL.Massively Sharded MySQL

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Vertical Partitioning Divide a table by relocating sets of columns • Not supported internally by MySQL, though you can do it manually by creating separate tables. • Not recommended in most cases – if your data is already normalized, then vertical partitioning introduces unnecessary joins • If your partitions are on different MySQL instances, then youʼre doing these “joins” in application code instead of in SQLMassively Sharded MySQL

10.
Functional Partitioning Divide a dataset by moving one or more tables • First eliminate all JOINs across tables in different partitions • Move tables to new partitions (separate MySQL instances) using selective dumping, followed by replication ﬁlters • Often just a temporary solution. If the table eventually grows too large to ﬁt on a single machine, youʼll need to shard it anyway.Massively Sharded MySQL

11.
When to Shard • Sharding is very complex, so itʼs best not to shard until itʼs obvious that you will actually need to! • Predict when you will hit write scalability issues — determine this on spare hardware • Predict when you will hit data size issues — calculate based on your growth rate • Functional partitioning can buy timeMassively Sharded MySQL

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Sharding Decisions • Sharding key — a core column present (or derivable) in most tables. • Sharding scheme — how you will group and home data (ranges vs hash vs lookup table) • How many shards to start with, or equivalently, how much data per shard • Shard colocation — do shards coexist within a DB schema, a MySQL instance, or a physical machine?Massively Sharded MySQL

13.
Sharding Schemes Determining which shard a row lives on • Ranges: Easy to implement and trivial to add new shards, but requires frequent and uneven rebalancing due to user behavior differences. • Hash or modulus: Apply function on the sharding key to determine which shard. Simple to implement, and distributes data evenly. Incredibly difﬁcult to add new shards or rebalance existing ones. • Lookup table: Highest ﬂexibility, but impacts performance and adds a single point of failure. Lookup table may eventually become too large.Massively Sharded MySQL

14.
Application Requirements • Sharding key must be available for all frequent look-up operations. For example, canʼt efﬁciently look up posts by their own ID anymore, also need blog ID to know which shard to hit. • Support for read-only and ofﬂine shards. App code needs to gracefully handle planned maintenance and unexpected failures. • Support for reading and writing to different MySQL instances for the same shard range — not for scaling reads, but for the rebalancing processMassively Sharded MySQL

17.
How to initially shard a table Option 1: Transitional migration with legacy DB • Choose a cutoff ID of the tableʼs PK (not the sharding key) which is slightly higher than its current max ID. Once that cutoff has been reached, all new rows get written to shards instead of legacy. • Whenever a legacy row is updated by app, move it to a shard • Migration script slowly saves old rows (at the app level) in the background, moving them to shards, and gradually lowers cutoff ID • Reads may need to check shards and legacy, but based on ID you can make an informed choice of which to check ﬁrstMassively Sharded MySQL

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How to initially shard a table Option 2: All at once 1. Dark mode: app redundantly sends all writes (inserts, updates, deletes) to legacy database as well as the appropriate shard. All reads still go to legacy database. 2. Migration: script reads data from legacy DB (sweeping by the sharding key) and writes it to the appropriate shard. 3. Finalize: move reads to shards, and then stop writing data to legacy.Massively Sharded MySQL

20.
Splitting shards: goals • Rebalance an overly-large shard by dividing it into N new shards, of even or uneven size • Speed • No locks • No application logic • Divide a 800gb shard (hundreds of millions of rows) in two in only 5 hours • Full read and write availability: shard-splitting process has no impact on live application performance, functionality, or data consistencyMassively Sharded MySQL

22.
Splitting shards: process Large “parent” shard divided into N “child” shards 1. Create N new slaves in parent shard pool — these will soon become masters of their own shard pools 2. Reduce the data set on those slaves so that each contains a different subset of the data 3. Move app reads from the parent to the appropriate children 4. Move app writes from the parent to the appropriate children 5. Stop replicating writes from the parent; take the parent pool ofﬂine 6. Remove rows that replicated to the wrong child shardMassively Sharded MySQL

23.
Splitting shards: process Large “parent” shard divided into N “child” shards 1. Create N new slaves in parent shard pool — these will soon become masters of their own shard pools 2. Reduce the data set on those slaves so that each contains a different subset of the data 3. Move app reads from the parent to the appropriate children 4. Move app writes from the parent to the appropriate children 5. Stop replicating writes from the parent; take the parent pool ofﬂine 6. Remove rows that replicated to the wrong child shardMassively Sharded MySQL

27.
Aside: copying ﬁles efﬁciently To multiple destinations • Add tee and a FIFO to the mix, and you can create a chained copy to multiple destinations simultaneously • Each box makes efﬁcient use of CPU, memory, disk, uplink, downlink • Performance penalty is only around 3% to 10% — much better than copying serially or from copying in parallel from a single source • http://engineering.tumblr.com/post/7658008285/efﬁciently-copying-ﬁles-to- multiple-destinationsMassively Sharded MySQL

28.
Splitting shards: process Large “parent” shard divided into N “child” shards 1. Create N new slaves in parent shard pool — these will soon become masters of their own shard pools 2. Reduce the data set on those slaves so that each contains a different subset of the data 3. Move app reads from the parent to the appropriate children 4. Move app writes from the parent to the appropriate children 5. Stop replicating writes from the parent; take the parent pool ofﬂine 6. Remove rows that replicated to the wrong child shardMassively Sharded MySQL

30.
Importing/exporting gotchas • Disable binary logging before import, to speed it up • Disable any query-killer scripts, or ﬁlter out SELECT ... INTO OUTFILE and LOAD DATA INFILE • Benchmark to ﬁgure out how many concurrent import/export queries can be run on your hardware • Be careful with disk I/O scheduler choices if using multiple disks with different speedsMassively Sharded MySQL

34.
Splitting shards: process Large “parent” shard divided into N “child” shards 1. Create N new slaves in parent shard pool — these will soon become masters of their own shard pools 2. Reduce the data set on those slaves so that each contains a different subset of the data 3. Move app reads from the parent to the appropriate children 4. Move app writes from the parent to the appropriate children 5. Stop replicating writes from the parent; take the parent pool ofﬂine 6. Remove rows that replicated to the wrong child shardMassively Sharded MySQL

35.
Moving reads and writes separately • If conﬁguration updates do not simultaneously reach all of your web/app servers, this would create consistency issues if reads/writes moved at same time • Web A gets new conﬁg, writes post for blog 200 to 1st child shard • Web B is still on old conﬁg, renders blog 200, reads from parent shard, doesnʼt ﬁnd the new post • Instead only move reads ﬁrst; let writes keep replicating from parent shard • After ﬁrst conﬁg update for reads, do a second one moving writes • Then wait for parent master binlog to stop moving before proceedingMassively Sharded MySQL

38.
Splitting shards: process Large “parent” shard divided into N “child” shards 1. Create N new slaves in parent shard pool — these will soon become masters of their own shard pools 2. Reduce the data set on those slaves so that each contains a different subset of the data 3. Move app reads from the parent to the appropriate children 4. Move app writes from the parent to the appropriate children 5. Stop replicating writes from the parent; take the parent pool ofﬂine 6. Remove rows that replicated to the wrong child shardMassively Sharded MySQL

41.
Splitting shards: process Large “parent” shard divided into N “child” shards 1. Create N new slaves in parent shard pool — these will soon become masters of their own shard pools 2. Reduce the data set on those slaves so that each contains a different subset of the data 3. Move app reads from the parent to the appropriate children 4. Move app writes from the parent to the appropriate children 5. Stop replicating writes from the parent; take the parent pool ofﬂine 6. Remove rows that replicated to the wrong child shardMassively Sharded MySQL

45.
Splitting shards: process Large “parent” shard divided into N “child” shards 1. Create N new slaves in parent shard pool — these will soon become masters of their own shard pools 2. Reduce the data set on those slaves so that each contains a different subset of the data 3. Move app reads from the parent to the appropriate children 4. Move app writes from the parent to the appropriate children 5. Stop replicating writes from the parent; take the parent pool ofﬂine 6. Remove rows that replicated to the wrong child shardMassively Sharded MySQL

46.
Splitting shards: cleanup • Until writes are moved to the new shard masters, writes to the parent shard replicate to all its child shards • Purge this data after shard split process is ﬁnished • Avoid huge single DELETE statements — they cause slave lag, among other issues (huge long transactions are generally bad) • Data is sparse, so itʼs efﬁcient to repeatedly do a SELECT (ﬁnd next sharding key value to clean) followed by a DELETE.Massively Sharded MySQL

47.
General sharding gotchas • Sizing shards properly: as small as you can operationally handle • Choosing ranges: try to keep them even, and better if they end in 000ʼs than 999ʼs or, worse yet, random ugly numbers. This matters once shard naming is based solely on the ranges. • Cutover: regularly create new shards to handle future data. Take the max value of your sharding key and add a cushion to it. • Before: highest blog ID 31.8 million, last shard handles range [30m, ∞) • After: that shard now handles [30m, 32m) and new last shard handles [32m, ∞)Massively Sharded MySQL